From manufacturing and entertainment to driving, robotics is one of the most exciting and evolving fields of technology. While engineers have historically programmed robots to complete specific tasks, robots are now being taught to learn on their own.

IEEE conducted interviews with robotics experts from all around the world and asked them to speak about how robots learn and to share demos of the robots they’ve worked with.

Our first video features IEEE Member Ming Liu, Assistant Professor at Hong Kong University of Science and Technology. His robot is a mobile, unmanned ground vehicle with visual sensors that allow it to perform autonomous driving tasks.

Liu’s speciality is mobile robotic navigation, as well as how robots perceive their environment, how they interact with humans and how they learn. He explains in the video that there are two ways that robots can learn, the traditional way or through deep data based learning. The traditional way uses the Simultaneous Localization Area Mapping (SLAM) program. This allows the robots to move in unknown environments and while it is moving, the robot is able to create a map of the area. The robot will learn in real time. Deep data based learning can take longer, as the robot starts with basic knowledge and collects experiences as it operates, building its own knowledge base.